Disclosed is an apparatus for generating a depth map using a monocular image. The apparatus includes: a deep convolution neural network (DCNN) optimized based on an encoder and decoder architecture. The encoder extracts one or more features from the monocular image according to the number of provided feature layers, and the decoder calculates displacements of mismatched pixels from the features extracted from different feature layers, and generates the depth map for the monocular image.
Legal claims defining the scope of protection, as filed with the USPTO.
2. The apparatus according to claim 1, wherein the encoder is based on MobileNetV2 to be mounted on a drone or a small robot for fast computation.
3. The apparatus according to claim 1, wherein the decoder performs decoding using all of the features extracted from an arbitrary feature layer provided in the encoder, the first major channel information, and the second major channel information.
4. The apparatus according to claim 1, wherein the decoder SE block is skip-connected to the encoder SE block.
5. The apparatus according to claim 1, wherein the DCNN includes a pose estimation network (PoseNet) and a depth estimation network (DepthNet) to learn data sets on a basis of unsupervised learning, and estimates a shape of an object in the monocular image.
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June 4, 2021
October 29, 2024
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